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                <h2 id="摘要"><a href="#摘要" class="headerlink" title="摘要"></a>摘要</h2><p>提示界面允许用户在视觉和语言上快速调整生成模型的输出。但是，提示中的小更改和设计选择可能会导致输出出现重大差异。在这项工作中，我们开发了一个黑盒框架，用于为非结构化图像和文本生成生成对抗性提示。这些提示可以是独立的，也可以附加在良性提示之前，将特定行为引入生成过程，例如生成特定对象的图像或生成高度困惑的文本。</p>
<h2 id="引言"><a href="#引言" class="headerlink" title="引言"></a>引言</h2><p>提示中的微小变化会导致模型输出的显着差异。例如，已知提示对句子的顺序、模板的变体以及提示中提供的具体示例很敏感。某些提示也可能导致奇怪或不寻常的行为。例如，生成的图像中的乱码，如“Apoploe vesrreaitais”，可以用作生成动物（如鸟类）的提示。</p>
<p>最近的工作已经看到研究人员制作了利用这些漏洞来提取信息或诱发恶意行为的对抗性提示。例如，Perez 和 Ribeiro 设计了在 LLM 中诱发特定行为的对抗性提示，例如重复其原始提示或输出特定字符串。Xu等研究了针对目标任务微调的 LLM 的后门和对抗性触发器。然而，后面这些对抗性攻击需要梯度信息和修改 LLM 权重的能力。对于用于各种非结构化任务的闭源通用 FM，例如 ChatGPT 或 DALL-E 2，此类攻击是不可能的。</p>
<p><strong>我们如何在只有查询访问权限的基础模型上为一般任务制作对抗性提示？</strong></p>
<p>在本文中，我们开发了一个框架，用于在黑盒设置中为一般语言和视觉目标寻找对抗性提示。主要挑战是代币空间的高维性和离散性。这些属性极大地增加了样本的复杂性，并减慢了经典黑盒对抗性攻击的收敛速度。为了解决这些问题，我们开发了一个令牌空间投影 (TSP) 来将一个连续的低维嵌入空间映射到一组离散的语言令牌。我们的主要贡献如下：</p>
<ol>
<li>我们开发了一个框架，用于使用令牌空间投影运算符查找对抗性提示。该运算符将连续词embedding空间与离散token空间联系起来，并允许使用黑盒攻击来查找对抗性提示。</li>
<li>我们演示了我们的框架如何自动查找导致文本到图像模型输出特定图像类的独立或前置提示。我们可以进一步找到排除与目标类相关的标记的对抗性提示。</li>
<li>我们的框架还可以找到改变文本生成的对抗性提示。</li>
</ol>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615151126517.png" alt="image-20230615151126517"></p>
<h2 id="对抗性提示优化"><a href="#对抗性提示优化" class="headerlink" title="对抗性提示优化"></a>对抗性提示优化</h2><p>语言模型中的对抗性攻击通常侧重于扰乱文本输入以改变模型的预测，同时保持原始语义。各种攻击策略包括引入拼写错误、替换单词和释义以扰乱原始文本。然而，这些攻击是为分类设置而设计的：它们需要一个干净的文本示例，然后对其进行修改。相比之下，没有这样干净的提示示例，它是从头开始设计的。</p>
<p>因此，我们寻求另一种类型的对抗性攻击来提示，一种可以诱导生成模型输出有针对性的变化。</p>
<p>我们的对抗性提示优化管道包括四个步骤：</p>
<p>1）生成候选词嵌入，<br>2）将嵌入投影到提示，<br>3）使用提示生成以获得损失，<br>4）使用无梯度优化技术来优化损失并提出新的候选词嵌入</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230614211408919.png" alt="image-20230614211408919"></p>
<p>定义adversarial prompt：</p>
<p>给定提示$p \in P$，模型 Mp 表示为响应 $r ∈ R$ 的概率分布，由 $p$ 参数化。</p>
<p>任意空间 $P$ 由具有最大长度 d 的token序列定义。我们将其表示为$P=T^d$，其中 T 表示离散令牌空间。</p>
<p>$T<em>{allow} ⊆ T$ 是允许标记的（可能受限的）子类，$P</em>{allow} := T^d<em>{allow} ⊆ P$ 是提示的（可能受限的）子类，由来自 $T</em>{allow}$ 的 d 个token组成.</p>
<p>因此，我们的对抗性提示框架旨在发现一个提示 p，它可以最大限度地减少生成响应分布的损失：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230614212957773.png" alt="image-20230614212957773"></p>
<p>我们将优化问题放宽为经验风险最小化框架 (ERM)，在该框架中我们观察响应$R^p_1, . . . , R^p_n  ∼ M_p$</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230614213129119.png" alt="image-20230614213129119"></p>
<h3 id="令牌空间投影"><a href="#令牌空间投影" class="headerlink" title="令牌空间投影"></a>令牌空间投影</h3><p>我们必须优化一个组合的大而离散的输入标记空间 $P = T^d$。为了解决这个问题，我们考虑与每个标记关联的词嵌入，而不是优化在连续嵌入空间上定义的上述公式。</p>
<p>考虑一个嵌入 $g :T→\xi $，其中对于每个标记 $t ∈ T$，我们有一个唯一的关联嵌入向量 $g(t) ∈ R^m$，其中 m 是嵌入维度。</p>
<p>由 d 个标记组成的任何提示 $p = (t_1, . . . , t_d) ∈ P$ 可以表示为嵌入 $g(p) := (g(t_1), . . . , g(t_d)) ∈ \xi^d ⊂ R^{m ×d}$.</p>
<p>我们可以立即优化嵌入空间 $\xi^d$ 而不是 P 并获得对抗性示例，但是，我们假设只能访问具有文本输入功能的黑盒生成模型，因此只对可用的文本提示感兴趣。</p>
<p>为了确保我们找到文本提示，我们将候选嵌入 e 投影到token，并使用投影的token评估损失函数。投影函数 $proj_S : \xi → S$</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615143452509.png" alt="image-20230615143452509"></p>
<p>为了符号方便，令 $q := proj<em>{P</em>{allow}}$</p>
<p>我们对优化问题的最终公式是：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615143616161.png" alt="image-20230615143616161"></p>
<h3 id="优化方法"><a href="#优化方法" class="headerlink" title="优化方法"></a>优化方法</h3><p>我们将上式中的最小化问题作为黑盒优化来解决，自然会导致应用标准技术来寻找最佳提示。</p>
<p>我们利用两种黑盒优化技术，即 Square Attack 算法，通常用于生成黑盒对抗性示例，以及高维贝叶斯优化的最新进展。</p>
<h4 id="Square-Attack"><a href="#Square-Attack" class="headerlink" title="Square Attack"></a>Square Attack</h4><p>Square Attack 算法是一种迭代算法，仅通过函数评估来优化任意高维黑盒 f。为简单起见，假设 $f : R^d → R $并且我们希望找到一个输入 X 来实现较低的 l 值。</p>
<p>设 $x_t$ 表示迭代 t 的候选向量，$x_0$ 是选择的初始化。 Square Attack 的迭代更新包括三个步骤：</p>
<ol>
<li>子集选择：选择要更新的 $x_t$ 索引的子集 S ⊆ [d]。令 $x^{(S)}_t$ 为 $x_t$ 的相应子集向量。</li>
<li>采样：采样$ v1，. . . , vk ∈ R^{|S|}$并创建 k 个新的候选向量。仅将 S 中的条目修改为 $v1, . . . , vk: x^{(S)}_{t,i} := vi$ 。</li>
<li>更新：用向量在$f (x<em>t,1), . . . , f (x_t,k)$和原始$f(x_t)$中获得的最低值的向量更新$x</em>{t+1}$</li>
</ol>
<p>在我们的实现中，对于子集选择步骤，我们选择一个随机子集 S，其中 |S| ≈ d/10。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615144352621.png" alt="image-20230615144352621"></p>
<p>Square Attack的算法细节：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615144423485.png" alt="image-20230615144423485"></p>
<p><em>random search（迭代优化方法</em>）：在每次迭代过程中<strong>随机采样</strong>一个扰动 δ , 如果 δ 能够提升目标函数，就保留这次的扰动。也就说对比传统的PGD, <em>random search</em>并<strong>不需要利用到梯度信息</strong>。</p>
<p>作者采用的random search的两个特点：</p>
<ul>
<li>每一步的扰动 δ 都刚好落在范数约束的边界上 $l_p$ 球的边界上。</li>
<li>在一次迭代过程中，只修改图像上一块局部的连续的正方形区域内的像素值。</li>
</ul>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615145352423.png" alt="image-20230615145352423"></p>
<h4 id="贝叶斯优化"><a href="#贝叶斯优化" class="headerlink" title="贝叶斯优化"></a>贝叶斯优化</h4><p>以大型基础模型为目标时，采样效率至关重要。贝叶斯优化 (BO) 是一种通用方法，用于解决嘈杂的黑盒优化问题，尤其是在需要样本效率时。</p>
<p>使用采集函数利用此预测后验来找到搜索空间中最有希望的候选者以进行下一步评估，从而有效地权衡探索和开发。当获取新数据时，替代模型会更新并逐渐变得更加准确。通过以这种方式顺序选择候选者进行评估，BO 可以减少优化昂贵的黑盒功能所需的评估次数。</p>
<p><strong>信赖域贝叶斯优化 (TuRBO)：</strong></p>
<p>埃里克森等人提出信赖域贝叶斯优化 (TuRBO)，它允许在更高维函数上使用 BO，而无需对搜索空间做出额外假设。</p>
<p>TuRBO 通过将搜索空间动态限制在超矩形信任区域内，从而减轻了通常困扰高维空间中 BO 算法的维数灾难，从而避免过度探索指数级大的搜索空间 X。</p>
<p>信任区域是输入空间 X 的超矩形子集，以优化器找到的最佳点为中心并且具有边长 $β<em>i ∈ [β</em>{min}, β<em>{max}]$。如果局部优化器连续改进了它自己的现有 $ρ</em>{succ}$ 次，则 βi 增加到 $min(2β<em>i, β</em>{max})$。类似地，当局部优化器连续失败 $ρ<em>{fail} $次时，长度 βi 减少到 β/2。如果 $β_i &lt; β</em>{min}$，则重新启动该本地优化器。</p>
<h2 id="损失和威胁模型"><a href="#损失和威胁模型" class="headerlink" title="损失和威胁模型"></a>损失和威胁模型</h2><p>为对抗性提示提供的公式是灵活的，可以适应各种提示类和损失函数。</p>
<p>提示类 $P_{allow}$ 的具体选择决定了所谓的来自对抗性示例的威胁模型，或允许对手用来构造提示的令牌空间。另一方面，损失函数 $l $指定了对手希望在生成的输出中引发的目标行为。指定允许的提示类别和损失函数完全定义了威胁模型和对抗性提示的目标。</p>
<h3 id="对抗目标"><a href="#对抗目标" class="headerlink" title="对抗目标"></a>对抗目标</h3><p><strong>分类器损失：</strong></p>
<p>为对抗性提示提供目标的一种方法是使用预训练分类器。然后，对手可以尝试找到将生成的文本或图像推向特定类别的提示。具体来说，分类器损失是来自分类器 g 的类别 CLS（例如猫、狗、汽车等）的负对数概率：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615150438472.png" alt="image-20230615150438472"></p>
<p>有了这种损失，对手的目的是迫使模型 $M_p$ 生成目标类 CLS 的图像。</p>
<p><strong>特征损失：</strong></p>
<p>我们还考虑了第二个损失函数，它使用生成的输出中的特征来捕获更细粒度的信息。例如，当 R 是生成的文本时，l(R) 可能是文本的困惑度，使用单独的语言模型计算。然后，对手会使用这种损失来找到提示，促使模型生成具有高度困惑的文本，可能是无意义的或乱码的文本。</p>
<h3 id="威胁模型"><a href="#威胁模型" class="headerlink" title="威胁模型"></a>威胁模型</h3><p>我们考虑以下三种类型的威胁模型来限制对手：</p>
<ol>
<li>无限制提示：对手是完全不受限制的 ($P_{allow} = P$)，可以使用任何token序列。</li>
<li>受限提示：对手仅限于仅使用令牌子集 $T_{allow} ⊂ T$ 的提示。这种受限的提示空间消除了简单地实现低损失的提示，例如仅包含类名的提示。</li>
<li>受限的前置提示：对手仅限于令牌的子集，并且必须将它们添加到预定义的提示 p’ 之前。更具体地说，$Ppre = ${$concat(p1, p’) : p1 ∈ Pallow$}其中 $Pallow$ 是任务 2 中的一组受限提示。</li>
</ol>
<p>这三种威胁模型增加了对手的约束和难度。特别是，第三个任务在提示中有相反的文本构造，对抗性提示必须在不使用相关词的情况下克服。</p>
<h2 id="实验"><a href="#实验" class="headerlink" title="实验"></a>实验</h2><p>重现所有结果的代码可在 <a target="_blank" rel="noopener" href="https://github.com/DebugML/adversarial_prompting">https://github.com/DebugML/adversarial_prompting</a> 获得。</p>
<h3 id="图像生成"><a href="#图像生成" class="headerlink" title="图像生成"></a>图像生成</h3><p><strong>最可能的类成功（MPC success）：</strong>如果优化器发现目标类是根据预训练的 ResNet18 分类模型生成的 5 张图像中的大多数的最大概率类的提示，则优化器实现 MPC 成功。</p>
<p><strong>超越简单的基线提示（OPB success）：</strong>用 CLS 表示目标 ImageNet 类。我们使用以下两个基线提示计算损失：<code>CLS</code> 和 <code>a picture of a CLS</code>.。如果优化器找到比这两个基线提示实现更低损失的提示，则优化器实现 OPB 成功。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615151526320.png" alt="image-20230615151526320"></p>
<p>上表为所有图像生成任务的 TuRBO 和 Square Attack 的成功率。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615151604502.png" alt="image-20230615151604502"></p>
<p>第一行包含优化提示生成的图像，并由 ResNet18 分类器分类为目标类。第二行包含从优化提示中的每个单独标记生成的图像，其中没有一个被归类为目标类</p>
<h3 id="文本生成"><a href="#文本生成" class="headerlink" title="文本生成"></a>文本生成</h3><p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615151731765.png" alt="image-20230615151731765"></p>
<h3 id="更多示例"><a href="#更多示例" class="headerlink" title="更多示例"></a>更多示例</h3><p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615151915805.png" alt="image-20230615151915805"></p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615151941461.png" alt="image-20230615151941461"></p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230615152015964.png" alt="image-20230615152015964"></p>

                
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